Foundations of Bayesian Estimation and Inference

Instructor: Milica Miočević
Software: R
Lectures: 4 hours
Software Demonstrations: 3 hours
Evergreen Content: Continually updated
Lifetime Access: Materials never expire

Class Overview Video

Professional: $259
Student: $199

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Category: Asynchronous, Workshops

Foundations of Bayesian Estimation and Inference introduces the theory and application of Bayesian statistics, using models for mean comparisons and linear regression as central examples. Bayesian methods have gained prominence in recent years as an alternative to classical approaches, offering more intuitive interpretations and addressing issues such as non-convergence in complex models. One of the most debated aspects of Bayesian analysis is the specification of prior distributions. This course explores strategies for incorporating existing information into priors and covers approaches for evaluating the prior implications and influence of priors on results. Participants will learn to conceptualize, specify, estimate, and interpret commonly used manifest variable models within the Bayesian framework using R.

1:57 Class Content / 7:05 Materials Provided / 8:03 Learning Objectives

Milica Miočević, Ph.D.

Milica Miočević is an Associate Professor in the Department of Psychology at McGill University where she teaches undergraduate and graduate courses on linear models, SEM, and applied Bayesian statistics. In the past decade, she has taught numerous workshops on mediation analysis and Bayesian statistics. Her research centers on Bayesian methods and statistical mediation analysis. Read More

Goals of the Workshop

The primary goal of the workshop is to provide an engaging learning experience that integrates the core theory of Bayesian statistics with its practical application to empirical data. Using examples focused on mean comparisons and linear regression, the workshop guides participants through each step of a Bayesian analysis—from prior specification and model estimation to model evaluation, comparison, inference, and sensitivity analysis. Participants will gain the knowledge, tools, and support needed to build a solid understanding of the Bayesian framework and to confidently apply Bayesian modeling techniques in their own research.

Who Should Attend

The workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences.

Although it is recommended that participants have a working knowledge of the general regression model, these core concepts will be reviewed at the beginning of the workshop. Participants who would like a more thorough refresher on linear regression prior to attending may wish to watch our (no cost) Linear Regression Playlist on YouTube.

Syllabus

Chapter 1. Introduction to Bayesian Statistics
1.1 Bayesian and Frequentist Statistics
1.2 Definitions of Probability and Bayes’ Theorem
1.3 Conjugate Priors and Analytical Solutions
1.4 Markov Chain Monte Carlo (MCMC)

Chapter 2. Inference in the Bayesian Framework
2.1 Normal Distribution Models for a Single Mean and Comparisons Between Multiple Means
2.2 Linear Regression Analysis
2.3 Multiple Regression Analysis and Bayesian Model Averaging (BMA)

Chapter 3. Model Evaluation
3.1 Posterior Predictive Checking (PPC)
3.2 Model Comparison
3.3 Bayes Factor

Chapter 4. Additional Considerations
4.1 Specification of Informative Prior Distributions
4.2 Sensitivity Analysis
4.3 Steps in a Bayesian Analysis
4.4 Sequential Updating of Findings Using Bayesian Analysis

Chapter 5. Extensions and Next Steps
5.1 Summary of Core Topics Covered
5.2 Where to Go from Here

Software Considerations

What is Provided

Participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.

Availability of Recordings

Certificate of Completion

After enrollment, you will have the option to generate a certificate of completion which will display your first and last name (as indicated in your user account), the name of the workshop, and the date upon which the certificate was generated. It can be printed to a PDF and will also be stored in the your user account for future access (after logging in to your account, select My Certificates from the user menu). Heck, you can even print out a hard copy and frame it to impress your friends.

Sample Content

Tuition Support for Students

We offer reduced-price registrations for undergraduate and graduate students who are actively enrolled in a recognized bachelor's, master's or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

A natural teacher. She was very clear in her explanations and always tuned into the learning experience for students.

Prof Miocevic is a great teacher because she is not only knowledgeable in statistics and quantitative methods but also because she can also teach it to ANYONE.

The course since the start was given extreme attention to detail and care to adapt the materials and classes to the multiple situations of the different individuals taking the class, all of this paired with the great dominion of all topics by the professor, aided in my understanding of the materials and helped me adapt to the course materials.

The pace of lectures was great with thorough and explicit explanations of all aspects of a concept rather than assumptions that students always knew ahead of time what symbols or values referred to. The lab portion of the course was very instructive. Scripts in R were very easy to follow and adapt as needed which gave us a chance to focus primarily on content while still learning some coding.

Professor Miocevic was a very articulate and detailed instructor. Her classes were clear and informative.

Professor Miocevic went above and beyond in presenting material, and even preliminary/supplemental material, for the class. Her slides and R–code were intuitive and well explained.

Foundations of Bayesian Estimation and Inference

Instructor

Milica Miočević, Ph.D.

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